{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Part 1 - Introduction to GeoEnrichment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction\n",
    "\n",
    "The GeoEnrichment module provides a python interface for access the demographic data provided through Business Analyst to enrich study areas, access standard geographies, and create reports. Accessing standard geographies enables retrieving and enriching standard jurisdictional areas such as counties, postal (zip) codes or US Census Block Groups in the United States."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **Note:** Organizations should review the [data attributions](https://www.esri.com/en-us/legal/terms/data-attributions) and [Master Agreement](https://www.esri.com/content/dam/esrisites/en-us/media/legal/ma-full/ma-full.pdf) to make sure they are in compliance when geoenriching data and making it available to other systems."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "### Enriching Study Areas\n",
    "\n",
    "The GeoEnrichment `enrich` capability adds location intelligence to the data by providing facts about a location or an area. Using GeoEnrichment, you can get information about the people and places in a specific area or within a certain distance or drive time from a location. It enables you to query and use information from a large collection of datasets including population, income, housing, consumer behavior, and the natural environment. \n",
    "\n",
    "This enables you to answer questions about locations that you can't answer with maps alone. For example, what kind of people live here? What do people like to do in this area? What are their habits and lifestyles?\n",
    "\n",
    "<img src=\"\">\n",
    "\n",
    "GeoEnrichment makes your analysis more powerful by adding demographic variables in a geographic context. Further, these variables can be accessed at multiple standard geographic resolutions based on the jurisdictional area."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Getting Started"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### GeoEnrichment Source\n",
    "\n",
    "Utilizing these capabilities requires either a properly configured Web GIS instance and a login with permissions to utilize these capabilities, or a local installation of [ArcGIS Pro with Business Analyst](https://doc.arcgis.com/en/business-analyst/desktop/introduction-to-esri-business-analyst-desktop-install-guide.htm) and at least one country [data pack installed](https://doc.arcgis.com/en/business-analyst/desktop/installing-business-analyst-data.htm). A properly configured Web GIS instance can be either ArcGIS Online or ArcGIS Enterprise. ArcGIS Enterprise can support GeoEnrichment module capabilities by configuring the GeoEnrichment utility service to [connect to ArcGIS Online](https://enterprise.arcgis.com/en/portal/latest/administer/windows/configure-services.htm#ESRI_SECTION2_1E0134BF60A049FFB388265B5A6AAE7F) or a fully configured [ArcGIS Business Analyst Enterprise](https://doc.arcgis.com/en/business-analyst/enterprise/latest/windows/business-analyst-enterprise-overview.htm) deployment."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Local GeoEnrichment Source\n",
    "\n",
    "If utilizing a local GIS to perform enrichment, you need to have an environment configured with ArcGIS Pro with Business Analyst and at least one local data pack. To specify this local source, you must create an `arcgis.gis.GIS` object instance using the `'pro'` keyword. This tells GeoEnrichment to use the locally installed source.\n",
    "\n",
    "**NOTE:** At the 2.1.0 release, standard geography retrieval and reporting is *not* yet supported with a local source."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from arcgis.gis import GIS\n",
    "\n",
    "gis = GIS(\"pro\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Web GIS GeoEnrichment Source\n",
    "\n",
    "If using ArcGIS Online or ArcGIS Enterprise with the GeoEnrichment module, you need to create an `arcgis.gis.GIS` object instance connected to the properly configured Web GIS with a user who has permissions to perform enrichment and create reports.\n",
    "\n",
    "**NOTE:** GeoEnrichment operations using ArcGIS Online consumes credits. Credits are the currency used across ArcGIS and are consumed for specific transactions. Learn more about credit consumption for GeoEnrichment [here](https://doc.arcgis.com/en/arcgis-online/administer/credits.htm#ESRI_SECTION1_709121D2C7694DCAB9B8592F36F7A5BA)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from arcgis.gis import GIS\n",
    "\n",
    "gis = GIS(profile=\"your_online_profile\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Discovering Countries\n",
    "\n",
    "Most of the data and jurisdictional areas are organized by country. First we will discover what countries are available. We set the optional `as_df` parameter to `True` in order to have a DataFrame returned as a result."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>iso2</th>\n",
       "      <th>iso3</th>\n",
       "      <th>name</th>\n",
       "      <th>alt_name</th>\n",
       "      <th>datasets</th>\n",
       "      <th>default_dataset</th>\n",
       "      <th>continent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AL</td>\n",
       "      <td>ALB</td>\n",
       "      <td>Albania</td>\n",
       "      <td>ALBANIA</td>\n",
       "      <td>[ALB_MBR_2021]</td>\n",
       "      <td>ALB_MBR_2021</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DZ</td>\n",
       "      <td>DZA</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>ALGERIA</td>\n",
       "      <td>[DZA_MBR_2021]</td>\n",
       "      <td>DZA_MBR_2021</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AD</td>\n",
       "      <td>AND</td>\n",
       "      <td>Andorra</td>\n",
       "      <td>ANDORRA</td>\n",
       "      <td>[AND_MBR_2021]</td>\n",
       "      <td>AND_MBR_2021</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AO</td>\n",
       "      <td>AGO</td>\n",
       "      <td>Angola</td>\n",
       "      <td>ANGOLA</td>\n",
       "      <td>[AGO_MBR_2021]</td>\n",
       "      <td>AGO_MBR_2021</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AI</td>\n",
       "      <td>AIA</td>\n",
       "      <td>Anguilla</td>\n",
       "      <td>ANGUILLA</td>\n",
       "      <td>[AIA_MBR_2020]</td>\n",
       "      <td>AIA_MBR_2020</td>\n",
       "      <td>North America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>VE</td>\n",
       "      <td>VEN</td>\n",
       "      <td>Venezuela</td>\n",
       "      <td>VENEZUELA, BOLIVARIAN REPUBLIC OF</td>\n",
       "      <td>[VEN_MBR_2021]</td>\n",
       "      <td>VEN_MBR_2021</td>\n",
       "      <td>South America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>VN</td>\n",
       "      <td>VNM</td>\n",
       "      <td>Vietnam</td>\n",
       "      <td>VIET NAM</td>\n",
       "      <td>[VNM_MBR_2022]</td>\n",
       "      <td>VNM_MBR_2022</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>VI</td>\n",
       "      <td>VIR</td>\n",
       "      <td>Virgin Islands</td>\n",
       "      <td>UNITED STATES VIRGIN ISLANDS</td>\n",
       "      <td>[VIR_MBR_2020]</td>\n",
       "      <td>VIR_MBR_2020</td>\n",
       "      <td>North America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>ZM</td>\n",
       "      <td>ZMB</td>\n",
       "      <td>Zambia</td>\n",
       "      <td>ZAMBIA</td>\n",
       "      <td>[ZMB_MBR_2021]</td>\n",
       "      <td>ZMB_MBR_2021</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>ZW</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>ZIMBABWE</td>\n",
       "      <td>[ZWE_MBR_2021]</td>\n",
       "      <td>ZWE_MBR_2021</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>177 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    iso2 iso3            name                           alt_name  \\\n",
       "0     AL  ALB         Albania                            ALBANIA   \n",
       "1     DZ  DZA         Algeria                            ALGERIA   \n",
       "2     AD  AND         Andorra                            ANDORRA   \n",
       "3     AO  AGO          Angola                             ANGOLA   \n",
       "4     AI  AIA        Anguilla                           ANGUILLA   \n",
       "..   ...  ...             ...                                ...   \n",
       "172   VE  VEN       Venezuela  VENEZUELA, BOLIVARIAN REPUBLIC OF   \n",
       "173   VN  VNM         Vietnam                           VIET NAM   \n",
       "174   VI  VIR  Virgin Islands       UNITED STATES VIRGIN ISLANDS   \n",
       "175   ZM  ZMB          Zambia                             ZAMBIA   \n",
       "176   ZW  ZWE        Zimbabwe                           ZIMBABWE   \n",
       "\n",
       "           datasets default_dataset      continent  \n",
       "0    [ALB_MBR_2021]    ALB_MBR_2021         Europe  \n",
       "1    [DZA_MBR_2021]    DZA_MBR_2021         Africa  \n",
       "2    [AND_MBR_2021]    AND_MBR_2021         Europe  \n",
       "3    [AGO_MBR_2021]    AGO_MBR_2021         Africa  \n",
       "4    [AIA_MBR_2020]    AIA_MBR_2020  North America  \n",
       "..              ...             ...            ...  \n",
       "172  [VEN_MBR_2021]    VEN_MBR_2021  South America  \n",
       "173  [VNM_MBR_2022]    VNM_MBR_2022           Asia  \n",
       "174  [VIR_MBR_2020]    VIR_MBR_2020  North America  \n",
       "175  [ZMB_MBR_2021]    ZMB_MBR_2021         Africa  \n",
       "176  [ZWE_MBR_2021]    ZWE_MBR_2021         Africa  \n",
       "\n",
       "[177 rows x 7 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from arcgis.geoenrichment import get_countries\n",
    "\n",
    "country_df = get_countries(gis, as_df=True)\n",
    "\n",
    "country_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, an `arcgis.geoenrichment.Country` object instance can be created to use for subsequent analysis steps."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Country - United States (GIS @ https://geosaurus.maps.arcgis.com version:10.3)>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from arcgis.geoenrichment import Country\n",
    "\n",
    "country = Country(\"usa\", gis=gis)\n",
    "\n",
    "country"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Enrich Example\n",
    "\n",
    "To provide context, we can apply a quick example:   \n",
    "A large retailer is evaluating potential sites for a new location. This retailer is interested in using key criteria to evaluate a few candidates. These criteria include competition, traffic, economic feasibility and market potential for the areas surroundinng the potential sites. Utilizing the GeoEnrichment module, the real estate site selection team can include demographic variables such as lifestyle, income, spending and education to understand potential customers in the study areas surrounding the candidate sites."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Discover Demographic Variables\n",
    "\n",
    "First, we can discover the variables available with the `enrich_variables` property of the `Country` object. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>alias</th>\n",
       "      <th>data_collection</th>\n",
       "      <th>enrich_name</th>\n",
       "      <th>enrich_field_name</th>\n",
       "      <th>description</th>\n",
       "      <th>vintage</th>\n",
       "      <th>units</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AGE0_CY</td>\n",
       "      <td>2022 Population Age &lt;1</td>\n",
       "      <td>1yearincrements</td>\n",
       "      <td>1yearincrements.AGE0_CY</td>\n",
       "      <td>F1yearincrements_AGE0_CY</td>\n",
       "      <td>2022 Total Population Age &lt;1 (Esri)</td>\n",
       "      <td>2022</td>\n",
       "      <td>count</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AGE1_CY</td>\n",
       "      <td>2022 Population Age 1</td>\n",
       "      <td>1yearincrements</td>\n",
       "      <td>1yearincrements.AGE1_CY</td>\n",
       "      <td>F1yearincrements_AGE1_CY</td>\n",
       "      <td>2022 Total Population Age 1 (Esri)</td>\n",
       "      <td>2022</td>\n",
       "      <td>count</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AGE2_CY</td>\n",
       "      <td>2022 Population Age 2</td>\n",
       "      <td>1yearincrements</td>\n",
       "      <td>1yearincrements.AGE2_CY</td>\n",
       "      <td>F1yearincrements_AGE2_CY</td>\n",
       "      <td>2022 Total Population Age 2 (Esri)</td>\n",
       "      <td>2022</td>\n",
       "      <td>count</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AGE3_CY</td>\n",
       "      <td>2022 Population Age 3</td>\n",
       "      <td>1yearincrements</td>\n",
       "      <td>1yearincrements.AGE3_CY</td>\n",
       "      <td>F1yearincrements_AGE3_CY</td>\n",
       "      <td>2022 Total Population Age 3 (Esri)</td>\n",
       "      <td>2022</td>\n",
       "      <td>count</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AGE4_CY</td>\n",
       "      <td>2022 Population Age 4</td>\n",
       "      <td>1yearincrements</td>\n",
       "      <td>1yearincrements.AGE4_CY</td>\n",
       "      <td>F1yearincrements_AGE4_CY</td>\n",
       "      <td>2022 Total Population Age 4 (Esri)</td>\n",
       "      <td>2022</td>\n",
       "      <td>count</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18941</th>\n",
       "      <td>MOEMEDYRMV</td>\n",
       "      <td>2020 Median Year Householder Moved In MOE (ACS...</td>\n",
       "      <td>yearmovedin</td>\n",
       "      <td>yearmovedin.MOEMEDYRMV</td>\n",
       "      <td>yearmovedin_MOEMEDYRMV</td>\n",
       "      <td>2020 Median Year Householder Moved into Unit M...</td>\n",
       "      <td>2016-2020</td>\n",
       "      <td>count</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18942</th>\n",
       "      <td>RELMEDYRMV</td>\n",
       "      <td>2020 Median Year Householder Moved In REL (ACS...</td>\n",
       "      <td>yearmovedin</td>\n",
       "      <td>yearmovedin.RELMEDYRMV</td>\n",
       "      <td>yearmovedin_RELMEDYRMV</td>\n",
       "      <td>2020 Median Year Householder Moved into Unit R...</td>\n",
       "      <td>2016-2020</td>\n",
       "      <td>count</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18943</th>\n",
       "      <td>ACSOWNER</td>\n",
       "      <td>2020 Owner Households (ACS 5-Yr)</td>\n",
       "      <td>yearmovedin</td>\n",
       "      <td>yearmovedin.ACSOWNER</td>\n",
       "      <td>yearmovedin_ACSOWNER</td>\n",
       "      <td>2020 Owner Households (ACS 5-Yr)</td>\n",
       "      <td>2016-2020</td>\n",
       "      <td>count</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18944</th>\n",
       "      <td>MOEOWNER</td>\n",
       "      <td>2020 Owner Households MOE (ACS 5-Yr)</td>\n",
       "      <td>yearmovedin</td>\n",
       "      <td>yearmovedin.MOEOWNER</td>\n",
       "      <td>yearmovedin_MOEOWNER</td>\n",
       "      <td>2020 Owner Households MOE (ACS 5-Yr)</td>\n",
       "      <td>2016-2020</td>\n",
       "      <td>count</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18945</th>\n",
       "      <td>RELOWNER</td>\n",
       "      <td>2020 Owner Households REL (ACS 5-Yr)</td>\n",
       "      <td>yearmovedin</td>\n",
       "      <td>yearmovedin.RELOWNER</td>\n",
       "      <td>yearmovedin_RELOWNER</td>\n",
       "      <td>2020 Owner Households REL (ACS 5-Yr)</td>\n",
       "      <td>2016-2020</td>\n",
       "      <td>count</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>18946 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             name                                              alias  \\\n",
       "0         AGE0_CY                             2022 Population Age <1   \n",
       "1         AGE1_CY                              2022 Population Age 1   \n",
       "2         AGE2_CY                              2022 Population Age 2   \n",
       "3         AGE3_CY                              2022 Population Age 3   \n",
       "4         AGE4_CY                              2022 Population Age 4   \n",
       "...           ...                                                ...   \n",
       "18941  MOEMEDYRMV  2020 Median Year Householder Moved In MOE (ACS...   \n",
       "18942  RELMEDYRMV  2020 Median Year Householder Moved In REL (ACS...   \n",
       "18943    ACSOWNER                   2020 Owner Households (ACS 5-Yr)   \n",
       "18944    MOEOWNER               2020 Owner Households MOE (ACS 5-Yr)   \n",
       "18945    RELOWNER               2020 Owner Households REL (ACS 5-Yr)   \n",
       "\n",
       "       data_collection              enrich_name         enrich_field_name  \\\n",
       "0      1yearincrements  1yearincrements.AGE0_CY  F1yearincrements_AGE0_CY   \n",
       "1      1yearincrements  1yearincrements.AGE1_CY  F1yearincrements_AGE1_CY   \n",
       "2      1yearincrements  1yearincrements.AGE2_CY  F1yearincrements_AGE2_CY   \n",
       "3      1yearincrements  1yearincrements.AGE3_CY  F1yearincrements_AGE3_CY   \n",
       "4      1yearincrements  1yearincrements.AGE4_CY  F1yearincrements_AGE4_CY   \n",
       "...                ...                      ...                       ...   \n",
       "18941      yearmovedin   yearmovedin.MOEMEDYRMV    yearmovedin_MOEMEDYRMV   \n",
       "18942      yearmovedin   yearmovedin.RELMEDYRMV    yearmovedin_RELMEDYRMV   \n",
       "18943      yearmovedin     yearmovedin.ACSOWNER      yearmovedin_ACSOWNER   \n",
       "18944      yearmovedin     yearmovedin.MOEOWNER      yearmovedin_MOEOWNER   \n",
       "18945      yearmovedin     yearmovedin.RELOWNER      yearmovedin_RELOWNER   \n",
       "\n",
       "                                             description    vintage  units  \n",
       "0                    2022 Total Population Age <1 (Esri)       2022  count  \n",
       "1                     2022 Total Population Age 1 (Esri)       2022  count  \n",
       "2                     2022 Total Population Age 2 (Esri)       2022  count  \n",
       "3                     2022 Total Population Age 3 (Esri)       2022  count  \n",
       "4                     2022 Total Population Age 4 (Esri)       2022  count  \n",
       "...                                                  ...        ...    ...  \n",
       "18941  2020 Median Year Householder Moved into Unit M...  2016-2020  count  \n",
       "18942  2020 Median Year Householder Moved into Unit R...  2016-2020  count  \n",
       "18943                   2020 Owner Households (ACS 5-Yr)  2016-2020  count  \n",
       "18944               2020 Owner Households MOE (ACS 5-Yr)  2016-2020  count  \n",
       "18945               2020 Owner Households REL (ACS 5-Yr)  2016-2020  count  \n",
       "\n",
       "[18946 rows x 8 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ev = country.enrich_variables\n",
    "\n",
    "ev"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Finding Variables\n",
    "\n",
    "This list of economic variables can be filtered using a few useful patterns. First, any variable ending with `CY` is a current year variable, so we can filter to just current year variables using this pattern."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>name</th>\n",
       "      <th>alias</th>\n",
       "      <th>data_collection</th>\n",
       "      <th>enrich_name</th>\n",
       "      <th>enrich_field_name</th>\n",
       "      <th>description</th>\n",
       "      <th>vintage</th>\n",
       "      <th>units</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>AGE0_CY</td>\n",
       "      <td>2022 Population Age &lt;1</td>\n",
       "      <td>1yearincrements</td>\n",
       "      <td>1yearincrements.AGE0_CY</td>\n",
       "      <td>F1yearincrements_AGE0_CY</td>\n",
       "      <td>2022 Total Population Age &lt;1 (Esri)</td>\n",
       "      <td>2022</td>\n",
       "      <td>count</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>AGE1_CY</td>\n",
       "      <td>2022 Population Age 1</td>\n",
       "      <td>1yearincrements</td>\n",
       "      <td>1yearincrements.AGE1_CY</td>\n",
       "      <td>F1yearincrements_AGE1_CY</td>\n",
       "      <td>2022 Total Population Age 1 (Esri)</td>\n",
       "      <td>2022</td>\n",
       "      <td>count</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>AGE2_CY</td>\n",
       "      <td>2022 Population Age 2</td>\n",
       "      <td>1yearincrements</td>\n",
       "      <td>1yearincrements.AGE2_CY</td>\n",
       "      <td>F1yearincrements_AGE2_CY</td>\n",
       "      <td>2022 Total Population Age 2 (Esri)</td>\n",
       "      <td>2022</td>\n",
       "      <td>count</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>AGE3_CY</td>\n",
       "      <td>2022 Population Age 3</td>\n",
       "      <td>1yearincrements</td>\n",
       "      <td>1yearincrements.AGE3_CY</td>\n",
       "      <td>F1yearincrements_AGE3_CY</td>\n",
       "      <td>2022 Total Population Age 3 (Esri)</td>\n",
       "      <td>2022</td>\n",
       "      <td>count</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>AGE4_CY</td>\n",
       "      <td>2022 Population Age 4</td>\n",
       "      <td>1yearincrements</td>\n",
       "      <td>1yearincrements.AGE4_CY</td>\n",
       "      <td>F1yearincrements_AGE4_CY</td>\n",
       "      <td>2022 Total Population Age 4 (Esri)</td>\n",
       "      <td>2022</td>\n",
       "      <td>count</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1595</th>\n",
       "      <td>18794</td>\n",
       "      <td>VAL1M_CY</td>\n",
       "      <td>2022 Home Value $1 Million-1499999</td>\n",
       "      <td>Wealth</td>\n",
       "      <td>Wealth.VAL1M_CY</td>\n",
       "      <td>Wealth_VAL1M_CY</td>\n",
       "      <td>2022 Home Value $1,000,000-$1,499,999 (Esri)</td>\n",
       "      <td>2022</td>\n",
       "      <td>count</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1596</th>\n",
       "      <td>18795</td>\n",
       "      <td>MEDVAL_CY</td>\n",
       "      <td>2022 Median Home Value</td>\n",
       "      <td>Wealth</td>\n",
       "      <td>Wealth.MEDVAL_CY</td>\n",
       "      <td>Wealth_MEDVAL_CY</td>\n",
       "      <td>2022 Median Home Value (Esri)</td>\n",
       "      <td>2022</td>\n",
       "      <td>currency</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1597</th>\n",
       "      <td>18796</td>\n",
       "      <td>AVGVAL_CY</td>\n",
       "      <td>2022 Average Home Value</td>\n",
       "      <td>Wealth</td>\n",
       "      <td>Wealth.AVGVAL_CY</td>\n",
       "      <td>Wealth_AVGVAL_CY</td>\n",
       "      <td>2022 Average Home Value (Esri)</td>\n",
       "      <td>2022</td>\n",
       "      <td>currency</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1598</th>\n",
       "      <td>18797</td>\n",
       "      <td>VALBASE_CY</td>\n",
       "      <td>2022 Home Value Base</td>\n",
       "      <td>Wealth</td>\n",
       "      <td>Wealth.VALBASE_CY</td>\n",
       "      <td>Wealth_VALBASE_CY</td>\n",
       "      <td>2022 Owner Occupied Housing Units by Value Bas...</td>\n",
       "      <td>2022</td>\n",
       "      <td>count</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1599</th>\n",
       "      <td>18827</td>\n",
       "      <td>WLTHINDXCY</td>\n",
       "      <td>2022 Wealth Index</td>\n",
       "      <td>Wealth</td>\n",
       "      <td>Wealth.WLTHINDXCY</td>\n",
       "      <td>Wealth_WLTHINDXCY</td>\n",
       "      <td>2022 Wealth Index (Esri)</td>\n",
       "      <td>2022</td>\n",
       "      <td>count</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1600 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      index        name                               alias  data_collection  \\\n",
       "0         0     AGE0_CY              2022 Population Age <1  1yearincrements   \n",
       "1         1     AGE1_CY               2022 Population Age 1  1yearincrements   \n",
       "2         2     AGE2_CY               2022 Population Age 2  1yearincrements   \n",
       "3         3     AGE3_CY               2022 Population Age 3  1yearincrements   \n",
       "4         4     AGE4_CY               2022 Population Age 4  1yearincrements   \n",
       "...     ...         ...                                 ...              ...   \n",
       "1595  18794    VAL1M_CY  2022 Home Value $1 Million-1499999           Wealth   \n",
       "1596  18795   MEDVAL_CY              2022 Median Home Value           Wealth   \n",
       "1597  18796   AVGVAL_CY             2022 Average Home Value           Wealth   \n",
       "1598  18797  VALBASE_CY                2022 Home Value Base           Wealth   \n",
       "1599  18827  WLTHINDXCY                   2022 Wealth Index           Wealth   \n",
       "\n",
       "                  enrich_name         enrich_field_name  \\\n",
       "0     1yearincrements.AGE0_CY  F1yearincrements_AGE0_CY   \n",
       "1     1yearincrements.AGE1_CY  F1yearincrements_AGE1_CY   \n",
       "2     1yearincrements.AGE2_CY  F1yearincrements_AGE2_CY   \n",
       "3     1yearincrements.AGE3_CY  F1yearincrements_AGE3_CY   \n",
       "4     1yearincrements.AGE4_CY  F1yearincrements_AGE4_CY   \n",
       "...                       ...                       ...   \n",
       "1595          Wealth.VAL1M_CY           Wealth_VAL1M_CY   \n",
       "1596         Wealth.MEDVAL_CY          Wealth_MEDVAL_CY   \n",
       "1597         Wealth.AVGVAL_CY          Wealth_AVGVAL_CY   \n",
       "1598        Wealth.VALBASE_CY         Wealth_VALBASE_CY   \n",
       "1599        Wealth.WLTHINDXCY         Wealth_WLTHINDXCY   \n",
       "\n",
       "                                            description vintage     units  \n",
       "0                   2022 Total Population Age <1 (Esri)    2022     count  \n",
       "1                    2022 Total Population Age 1 (Esri)    2022     count  \n",
       "2                    2022 Total Population Age 2 (Esri)    2022     count  \n",
       "3                    2022 Total Population Age 3 (Esri)    2022     count  \n",
       "4                    2022 Total Population Age 4 (Esri)    2022     count  \n",
       "...                                                 ...     ...       ...  \n",
       "1595       2022 Home Value $1,000,000-$1,499,999 (Esri)    2022     count  \n",
       "1596                      2022 Median Home Value (Esri)    2022  currency  \n",
       "1597                     2022 Average Home Value (Esri)    2022  currency  \n",
       "1598  2022 Owner Occupied Housing Units by Value Bas...    2022     count  \n",
       "1599                           2022 Wealth Index (Esri)    2022     count  \n",
       "\n",
       "[1600 rows x 9 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ev[ev.name.str.lower().str.contains(\"cy\")].reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because we are working with a DataFrame, we can easily filter by key words in the description. Here, we are searching for a metric representing relative diversity. We see there is a variable available, the 2021 Diversity Index. There are three rows that result from our filtering.\n",
    "\n",
    "Data Collections are groupings of variables. Frequently these groupings can speed up analysis by offering a selection of variables to use for quickly getting started."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>alias</th>\n",
       "      <th>data_collection</th>\n",
       "      <th>enrich_name</th>\n",
       "      <th>enrich_field_name</th>\n",
       "      <th>description</th>\n",
       "      <th>vintage</th>\n",
       "      <th>units</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>DIVINDX_CY</td>\n",
       "      <td>2022 Diversity Index</td>\n",
       "      <td>KeyUSFacts</td>\n",
       "      <td>KeyUSFacts.DIVINDX_CY</td>\n",
       "      <td>KeyUSFacts_DIVINDX_CY</td>\n",
       "      <td>2022 Diversity Index (Esri)</td>\n",
       "      <td>2022</td>\n",
       "      <td>count</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DIVINDX_CY</td>\n",
       "      <td>2022 Diversity Index</td>\n",
       "      <td>Policy</td>\n",
       "      <td>Policy.DIVINDX_CY</td>\n",
       "      <td>Policy_DIVINDX_CY</td>\n",
       "      <td>2022 Diversity Index (Esri)</td>\n",
       "      <td>2022</td>\n",
       "      <td>count</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>DIVINDX_CY</td>\n",
       "      <td>2022 Diversity Index</td>\n",
       "      <td>raceandhispanicorigin</td>\n",
       "      <td>raceandhispanicorigin.DIVINDX_CY</td>\n",
       "      <td>raceandhispanicorigin_DIVINDX_CY</td>\n",
       "      <td>2022 Diversity Index (Esri)</td>\n",
       "      <td>2022</td>\n",
       "      <td>count</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         name                 alias        data_collection  \\\n",
       "0  DIVINDX_CY  2022 Diversity Index             KeyUSFacts   \n",
       "1  DIVINDX_CY  2022 Diversity Index                 Policy   \n",
       "2  DIVINDX_CY  2022 Diversity Index  raceandhispanicorigin   \n",
       "\n",
       "                        enrich_name                 enrich_field_name  \\\n",
       "0             KeyUSFacts.DIVINDX_CY             KeyUSFacts_DIVINDX_CY   \n",
       "1                 Policy.DIVINDX_CY                 Policy_DIVINDX_CY   \n",
       "2  raceandhispanicorigin.DIVINDX_CY  raceandhispanicorigin_DIVINDX_CY   \n",
       "\n",
       "                   description vintage  units  \n",
       "0  2022 Diversity Index (Esri)    2022  count  \n",
       "1  2022 Diversity Index (Esri)    2022  count  \n",
       "2  2022 Diversity Index (Esri)    2022  count  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ev[\n",
    "    (ev.name.str.lower().str.contains(\"cy\"))\n",
    "    & (ev.alias.str.lower().str.contains(\"diversity\"))\n",
    "].reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we can select a few variables to use for analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['TOTPOP_CY',\n",
       " 'DIVINDX_CY',\n",
       " 'AVGHHSZ_CY',\n",
       " 'MEDAGE_CY',\n",
       " 'MEDHINC_CY',\n",
       " 'BACHDEG_CY']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "analysis_variables = [\n",
    "    \"TOTPOP_CY\",  # Population: Total Population (Esri)\n",
    "    \"DIVINDX_CY\",  # Diversity Index (Esri)\n",
    "    \"AVGHHSZ_CY\",  # Average Household Size (Esri)\n",
    "    \"MEDAGE_CY\",  # Age: Median Age (Esri)\n",
    "    \"MEDHINC_CY\",  # Income: Median Household Income (Esri)\n",
    "    \"BACHDEG_CY\",  # Education: Bachelor\"s Degree (Esri)\n",
    "]\n",
    "\n",
    "analysis_variables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load Data\n",
    "\n",
    "We can load the study areas surrounding each location from a Python pickle file. The enrich capability in Business Analyst requires polygon areas to use for [apportioning demographic data](https://developers.arcgis.com/rest/geoenrichment/api-reference/data-apportionment.htm) to the input geographies. The polygons delineating the area to be used for apportioning selected demographic data to each location, these are referred to as *study areas*. While, for this example, we already have study areas created, it is possible to specify parameters for study areas for the enrich tool. This is demonstrated in a later example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>OBJECTID</th>\n",
       "      <th>loc_id</th>\n",
       "      <th>SHAPE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Facility 1</td>\n",
       "      <td>{\"rings\": [[[-118.309153568, 34.074037262], [-...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Facility 2</td>\n",
       "      <td>{\"rings\": [[[-118.309153568, 34.082122063], [-...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Facility 4</td>\n",
       "      <td>{\"rings\": [[[-118.376302328, 34.090880596], [-...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Facility 5</td>\n",
       "      <td>{\"rings\": [[[-118.376302328, 34.0911051740001]...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Facility 3</td>\n",
       "      <td>{\"rings\": [[[-118.153970313, 34.0778550840001]...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   OBJECTID      loc_id                                              SHAPE\n",
       "0         1  Facility 1  {\"rings\": [[[-118.309153568, 34.074037262], [-...\n",
       "1         2  Facility 2  {\"rings\": [[[-118.309153568, 34.082122063], [-...\n",
       "2         3  Facility 4  {\"rings\": [[[-118.376302328, 34.090880596], [-...\n",
       "3         4  Facility 5  {\"rings\": [[[-118.376302328, 34.0911051740001]...\n",
       "4         5  Facility 3  {\"rings\": [[[-118.153970313, 34.0778550840001]..."
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from arcgis.features import (\n",
    "    GeoAccessor,  # adds \"spatial\" namespace to Pandas DataFrame object\n",
    ")\n",
    "\n",
    "itm_id = \"379bdcc3f34b4407bef1135956edcf4b\"\n",
    "candidate_df = (\n",
    "    gis.content.get(itm_id).layers[0].query(out_fields=\"loc_id\", as_df=True)\n",
    ")\n",
    "\n",
    "candidate_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Enrich"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we can run the `enrich` method found in the Country class to get data about the study areas using the enrich variables selected above. If you are enriching a study area where you do not know the country you can also use the `enrich` method found outside of the Country class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>objectid</th>\n",
       "      <th>loc_id</th>\n",
       "      <th>source_country</th>\n",
       "      <th>aggregation_method</th>\n",
       "      <th>population_to_polygon_size_rating</th>\n",
       "      <th>apportionment_confidence</th>\n",
       "      <th>has_data</th>\n",
       "      <th>medage_cy</th>\n",
       "      <th>totpop_cy</th>\n",
       "      <th>avghhsz_cy</th>\n",
       "      <th>bachdeg_cy</th>\n",
       "      <th>medhinc_cy</th>\n",
       "      <th>divindx_cy</th>\n",
       "      <th>SHAPE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Facility 1</td>\n",
       "      <td>USA</td>\n",
       "      <td>BlockApportionment:US.BlockGroups;PointsLayer:...</td>\n",
       "      <td>2.191</td>\n",
       "      <td>2.576</td>\n",
       "      <td>1</td>\n",
       "      <td>33.7</td>\n",
       "      <td>441440.0</td>\n",
       "      <td>2.61</td>\n",
       "      <td>61913.0</td>\n",
       "      <td>50088.0</td>\n",
       "      <td>88.4</td>\n",
       "      <td>{\"rings\": [[[-118.309153568, 34.07403726200000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Facility 2</td>\n",
       "      <td>USA</td>\n",
       "      <td>BlockApportionment:US.BlockGroups;PointsLayer:...</td>\n",
       "      <td>2.191</td>\n",
       "      <td>2.576</td>\n",
       "      <td>1</td>\n",
       "      <td>34.3</td>\n",
       "      <td>454965.0</td>\n",
       "      <td>2.52</td>\n",
       "      <td>72400.0</td>\n",
       "      <td>52284.0</td>\n",
       "      <td>88.7</td>\n",
       "      <td>{\"rings\": [[[-118.309153568, 34.082122063], [-...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Facility 4</td>\n",
       "      <td>USA</td>\n",
       "      <td>BlockApportionment:US.BlockGroups;PointsLayer:...</td>\n",
       "      <td>2.191</td>\n",
       "      <td>2.576</td>\n",
       "      <td>1</td>\n",
       "      <td>38.4</td>\n",
       "      <td>224109.0</td>\n",
       "      <td>2.23</td>\n",
       "      <td>58747.0</td>\n",
       "      <td>93112.0</td>\n",
       "      <td>82.2</td>\n",
       "      <td>{\"rings\": [[[-118.376302328, 34.09088059599999...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Facility 5</td>\n",
       "      <td>USA</td>\n",
       "      <td>BlockApportionment:US.BlockGroups;PointsLayer:...</td>\n",
       "      <td>2.191</td>\n",
       "      <td>2.576</td>\n",
       "      <td>1</td>\n",
       "      <td>38.5</td>\n",
       "      <td>221385.0</td>\n",
       "      <td>2.20</td>\n",
       "      <td>58655.0</td>\n",
       "      <td>94416.0</td>\n",
       "      <td>81.5</td>\n",
       "      <td>{\"rings\": [[[-118.376302328, 34.0911051740001]...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Facility 3</td>\n",
       "      <td>USA</td>\n",
       "      <td>BlockApportionment:US.BlockGroups;PointsLayer:...</td>\n",
       "      <td>2.191</td>\n",
       "      <td>2.576</td>\n",
       "      <td>1</td>\n",
       "      <td>31.4</td>\n",
       "      <td>230872.0</td>\n",
       "      <td>3.51</td>\n",
       "      <td>16674.0</td>\n",
       "      <td>55399.0</td>\n",
       "      <td>71.2</td>\n",
       "      <td>{\"rings\": [[[-118.15397031299999, 34.077855084...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   objectid      loc_id source_country  \\\n",
       "0         1  Facility 1            USA   \n",
       "1         2  Facility 2            USA   \n",
       "2         3  Facility 4            USA   \n",
       "3         4  Facility 5            USA   \n",
       "4         5  Facility 3            USA   \n",
       "\n",
       "                                  aggregation_method  \\\n",
       "0  BlockApportionment:US.BlockGroups;PointsLayer:...   \n",
       "1  BlockApportionment:US.BlockGroups;PointsLayer:...   \n",
       "2  BlockApportionment:US.BlockGroups;PointsLayer:...   \n",
       "3  BlockApportionment:US.BlockGroups;PointsLayer:...   \n",
       "4  BlockApportionment:US.BlockGroups;PointsLayer:...   \n",
       "\n",
       "   population_to_polygon_size_rating  apportionment_confidence  has_data  \\\n",
       "0                              2.191                     2.576         1   \n",
       "1                              2.191                     2.576         1   \n",
       "2                              2.191                     2.576         1   \n",
       "3                              2.191                     2.576         1   \n",
       "4                              2.191                     2.576         1   \n",
       "\n",
       "   medage_cy  totpop_cy  avghhsz_cy  bachdeg_cy  medhinc_cy  divindx_cy  \\\n",
       "0       33.7   441440.0        2.61     61913.0     50088.0        88.4   \n",
       "1       34.3   454965.0        2.52     72400.0     52284.0        88.7   \n",
       "2       38.4   224109.0        2.23     58747.0     93112.0        82.2   \n",
       "3       38.5   221385.0        2.20     58655.0     94416.0        81.5   \n",
       "4       31.4   230872.0        3.51     16674.0     55399.0        71.2   \n",
       "\n",
       "                                               SHAPE  \n",
       "0  {\"rings\": [[[-118.309153568, 34.07403726200000...  \n",
       "1  {\"rings\": [[[-118.309153568, 34.082122063], [-...  \n",
       "2  {\"rings\": [[[-118.376302328, 34.09088059599999...  \n",
       "3  {\"rings\": [[[-118.376302328, 34.0911051740001]...  \n",
       "4  {\"rings\": [[[-118.15397031299999, 34.077855084...  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "enrich_df = country.enrich(candidate_df, enrich_variables=analysis_variables)\n",
    "\n",
    "enrich_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The response includes metadata related to how the enrichment was performed. However, if we are only interested in the actual demographic columns added, we can filter to just these using the available enrich variable names."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>medage_cy</th>\n",
       "      <th>totpop_cy</th>\n",
       "      <th>avghhsz_cy</th>\n",
       "      <th>bachdeg_cy</th>\n",
       "      <th>medhinc_cy</th>\n",
       "      <th>divindx_cy</th>\n",
       "      <th>SHAPE</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loc_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Facility 1</th>\n",
       "      <td>33.7</td>\n",
       "      <td>441440.0</td>\n",
       "      <td>2.61</td>\n",
       "      <td>61913.0</td>\n",
       "      <td>50088.0</td>\n",
       "      <td>88.4</td>\n",
       "      <td>{\"rings\": [[[-118.309153568, 34.07403726200000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Facility 2</th>\n",
       "      <td>34.3</td>\n",
       "      <td>454965.0</td>\n",
       "      <td>2.52</td>\n",
       "      <td>72400.0</td>\n",
       "      <td>52284.0</td>\n",
       "      <td>88.7</td>\n",
       "      <td>{\"rings\": [[[-118.309153568, 34.082122063], [-...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Facility 4</th>\n",
       "      <td>38.4</td>\n",
       "      <td>224109.0</td>\n",
       "      <td>2.23</td>\n",
       "      <td>58747.0</td>\n",
       "      <td>93112.0</td>\n",
       "      <td>82.2</td>\n",
       "      <td>{\"rings\": [[[-118.376302328, 34.09088059599999...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Facility 5</th>\n",
       "      <td>38.5</td>\n",
       "      <td>221385.0</td>\n",
       "      <td>2.20</td>\n",
       "      <td>58655.0</td>\n",
       "      <td>94416.0</td>\n",
       "      <td>81.5</td>\n",
       "      <td>{\"rings\": [[[-118.376302328, 34.0911051740001]...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Facility 3</th>\n",
       "      <td>31.4</td>\n",
       "      <td>230872.0</td>\n",
       "      <td>3.51</td>\n",
       "      <td>16674.0</td>\n",
       "      <td>55399.0</td>\n",
       "      <td>71.2</td>\n",
       "      <td>{\"rings\": [[[-118.15397031299999, 34.077855084...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            medage_cy  totpop_cy  avghhsz_cy  bachdeg_cy  medhinc_cy  \\\n",
       "loc_id                                                                 \n",
       "Facility 1       33.7   441440.0        2.61     61913.0     50088.0   \n",
       "Facility 2       34.3   454965.0        2.52     72400.0     52284.0   \n",
       "Facility 4       38.4   224109.0        2.23     58747.0     93112.0   \n",
       "Facility 5       38.5   221385.0        2.20     58655.0     94416.0   \n",
       "Facility 3       31.4   230872.0        3.51     16674.0     55399.0   \n",
       "\n",
       "            divindx_cy                                              SHAPE  \n",
       "loc_id                                                                     \n",
       "Facility 1        88.4  {\"rings\": [[[-118.309153568, 34.07403726200000...  \n",
       "Facility 2        88.7  {\"rings\": [[[-118.309153568, 34.082122063], [-...  \n",
       "Facility 4        82.2  {\"rings\": [[[-118.376302328, 34.09088059599999...  \n",
       "Facility 5        81.5  {\"rings\": [[[-118.376302328, 34.0911051740001]...  \n",
       "Facility 3        71.2  {\"rings\": [[[-118.15397031299999, 34.077855084...  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get just the enrich columns\n",
    "enrich_cols = [c for c in enrich_df if c in ev.name.str.lower().values]\n",
    "\n",
    "# combine the enrich columns with a few others we want to keep\n",
    "keep_cols = [\"loc_id\"] + enrich_cols + [\"SHAPE\"]\n",
    "\n",
    "# filter the enrich data frame to just these columns\n",
    "enrich_df = enrich_df.loc[:, keep_cols].set_index(\"loc_id\")\n",
    "\n",
    "# re-enable spatial awareness\n",
    "enrich_df.spatial.set_geometry(\"SHAPE\")\n",
    "\n",
    "enrich_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluate Results\n",
    "\n",
    "An extremely effective starting point for analysis is simply visualizing the results. Here, we are using `matplotlib` to visualize the differencees between the locations based on the enriched data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x720 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# this is due to a deprication warning inside matplotlib\n",
    "import warnings\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "fig, axs = plt.subplots(2, 3)\n",
    "fig.set_figheight(10.0)\n",
    "fig.set_figwidth(18.0)\n",
    "fig.subplots_adjust(hspace=0.4)\n",
    "\n",
    "plt.sca(axs[0, 0])\n",
    "_ = enrich_df.medage_cy.plot(title=\"Median Age\", kind=\"bar\")\n",
    "\n",
    "plt.sca(axs[0, 1])\n",
    "_ = enrich_df.totpop_cy.plot(title=\"Total Population\", kind=\"bar\")\n",
    "\n",
    "plt.sca(axs[0, 2])\n",
    "_ = enrich_df.avghhsz_cy.plot(title=\"Average Household Size\", kind=\"bar\")\n",
    "\n",
    "plt.sca(axs[1, 0])\n",
    "_ = enrich_df.bachdeg_cy.plot(title=\"Bachelor's Degree\", kind=\"bar\")\n",
    "\n",
    "plt.sca(axs[1, 1])\n",
    "_ = enrich_df.medhinc_cy.plot(title=\"Median Household Income\", kind=\"bar\")\n",
    "\n",
    "plt.sca(axs[1, 2])\n",
    "_ = enrich_df.divindx_cy.plot(title=\"Diversity Index\", kind=\"bar\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Facility 1 and facility 2 have higher populations, and are diverse with less income. Facility 3 is far younger with larger households, less education, and have lower incomes. Facility 4 and facility 5 are older, more educated and have a higher income.\n",
    "\n",
    "If interested in opening a discount department store, facility 2 is the most attractive location with facility 1 as a close second. The diversity and lower income can allow us to conclude that people will buy at lower prices.\n",
    "\n",
    "If interested in opening a quick service restaurant, facility 3 may be the best option to meet the needs of a young, busy and price conscious population.\n",
    "\n",
    "Obviously, depending on the key characteristics of the business looking for a new location, the key demographic indicators will be different. Using geoenrichment, paired with the ArcGIS API for Python, enables extremely quick access to demographic variables for informed decision making."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion  \n",
    "  \n",
    "    \n",
    "GeoEnrichment makes any location data intelligent by providing facts about the location. In this part of the Geoenrichment guide series, you have seen a high-level example of how `arcgis.geoenrichment.Country` country can be used to `enrich` a dataset with various socio-demographic features, and also an introduction of the different ways in which data can be enriched. In the subsequent pages, you will learn about:\n",
    "1. Enriching Study Areas (explains where to enrich)\n",
    "2. Exploring Named Statistical Areas (explains where to enrich continued)\n",
    "3. Enriching Data Collections and Spatially Enabled Dataframe (explains what datasets/variables to enrich with)\n",
    "4. Generating Reports\n",
    "5. Standard Geography Queries"
   ]
  }
 ],
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